With the development of modern science and technology, the complexity of process industries such as a chemical process, a refining process or a biopharmaceutical process has been increasing. More and more auxiliary devices such as a distributed control system (DCS) or a manufacturing execution system (MES) have been widely used for monitoring an on-line production operation so as to ensure the stable and safe running of a technological process. At the same time, with the improvement of a production automation level, the number of operators in a plant is greatly reduced in recent years than in the past years, so that one operator may need to operate one or even more production units or devices. A simple variable alarm is insufficient to provide the most direct signal for the operator to handle emergency situations, and the operator needs to determine a possible state of the technological process with a lot of experience, which may cause more serious consequences due to the misjudgment or operating lag of a less experienced operator.
Currently, a separate fault diagnosing system is established to detect and diagnose a cause of a fault and to show the type of a fault which may occur to the operator through an interface, which may not only ensure the stability of a production running and prevent the occurrence of major accidents but also may help the operator to handle and repair the fault so as to reduce losses caused by the fault effectively.
Firstly, a perfect on-line fault diagnosing system must be able to quickly detect a fault after a disturbance occurs and be able to accurately diagnose the possible type of the fault based on a rapid effective fault diagnosing method. Secondly, the on-line fault diagnosing system must be able to have a complete structure, be able to acquire data from an on-line running device, and be able to show a diagnostic result to the operator through a friendly interface after a core diagnosis is completed. Finally, the fault diagnosing method must have a self-adaptive capacity and a self-learning ability, and the on-line fault diagnosing system must use on-line data to achieve the self-learning of the on-line fault diagnosing system and to improve the on-line fault diagnosing capability of the on-line fault diagnosing system according to the feedback of the operator.
An artificial immune system is an integrated intelligent system, which combines immunology with engineering and uses mathematics, computer technology, etc. to establish an immune mechanism model and applies the immune mechanism model to the design, implementation, etc. of a technological process. Judgment of self and non-self in the artificial immune system is introduced into the field of a fault diagnosis. A dynamic artificial immune system is configured to perform an on-line fault diagnosis for a technological process by calculating a difference degree of an antigen and an antibody using dynamic variable data of the technological process as a drive, using a historical data time sequence matrix as the antibody and using an on-line data time sequence matrix as the antigen on account of the dynamic characteristics of the technological process.
However, many new technological processes have not experienced a long-time running and lack available historical samples. In this case, due to the lack of historical data, the dynamic artificial immune system may not diagnose a fault effectively. Accordingly, it is necessary to propose a newer mechanism to produce an antibody which may be used for a dynamic artificial immune fault diagnosis.